Developing a Visual Diagnostic System to Reduce Waiting Times for Image-Recognisable Diseases
This project leverages AI and signal processing to rapidly diagnose visually recognizable diseases through images, such as skin cancer, viral skin infections, dermatological conditions, trauma, and oral lesions.
By automating initial screenings, it reduces waiting times, prioritizes urgent cases, and improves healthcare efficiency through faster and more accessible diagnosis. Its significance lies in enabling early detection and prompt intervention, which are crucial for effective treatment, preventing disease progression, reducing complications, and ultimately saving lives.
Intelligent Pipeline Integrity Monitoring & Wireless Telemetry System
This project involves the development of a high-performance remote monitoring system for oil and gas pipeline infrastructure. It utilizes advanced acoustic sensors and vibration analysis to detect leaks and structural anomalies in real-time. The system features hardware-accelerated signal conditioning and filtering implemented on FPGA to ensure low-latency data processing. To maintain reliable connectivity in remote environments, the project integrates robust wireless communication links with optimized link-level analysis and error-correction coding, ensuring seamless data transmission to central control facilities.
By enhancing pipeline safety and operational efficiency, this system can reduce maintenance costs, mitigate environmental risks, and ensure energy security through proactive infrastructure monitoring.
FPGA-Accelerated Real-time Biomedical Signal Processing for Remote Health Monitoring
This project develops a portable medical monitoring platform using FPGA-based hardware for real-time analysis of cardiac and vital signs. It employs high-speed DSP algorithms for noise reduction and feature extraction, with secure, power-efficient wireless data transmission. By processing sensor signals locally, it improves diagnostic accuracy and enables rapid clinical responses.
This technology offers NHS hospitals a reliable, cost-effective solution for remote patient monitoring, enhancing early diagnosis and reducing hospital workload.
